Gambling on putative biomarkers of osteoarthritis and osteochondrosis by equine synovial fluid proteomics

Osteoarthritis (OA) and osteochondrosis (OC) are two of the main challenges in orthopedics, whose definitive diagnosis is usually based on radiographic/arthroscopic evidences. Their early diagnosis should allow preventive or timely therapeutic actions, which are generally precluded from the poor relationships occurring between symptomatologic and radiographic evidences. These limitations should be overcome by improving the knowledge on articular tissue metabolism and on molecular factors regulating its normal homeostasis, also identifying novel OA and OC biomarkers suitable for their earlier diagnoses, whenever clinical/pathological inflammatory scenarios between these joint diseases seem somewhat related. To identify proteins involved in their aetiology and progression, we undertook a differential proteomic analysis of equine synovial fluid (SF), which compared the protein pattern of OA or OC patients with that of healthy individuals. Deregulated proteins in OA and OC included components related to inflammatory state, coagulation pathways, oxidative stress and matrix damage, which were suggestive of pathological alterations in articular homeostasis, plasma-SF exchange, joint nutritional status and vessel permeability. Some proteins seemed commonly deregulated in both pathologies indicating that, regardless of the stimulus, common pathways are affected and/or the animal joint uses the same molecular mechanisms to restore its homeostasis. On the other hand, the increased number of deregulated proteins observed in OA with respect to OC, together with their nature, confirmed the high inflammatory character of this disease. Some deregulated proteins in OA found a verification by analyzing the SF of injured arthritic joints following autologous conditioned serum treatment, an emergent therapy that provides positive results for both human and equine OA. Being the horse involved in occupational/sporting activities and considered as an excellent animal model for human joint diseases, our data provide suggestive information for tentative biomedical extrapolations, allowing to overcome the limitations in joint size and workload that are typical of other small animal models.     

Effect of sleep stage on breathing in children with central hypoventilation.

The early literature suggests that hypoventilation in infants with congenital central hypoventilation syndrome (CHS) is less severe during rapid eye movement (REM) than during non-REM (NREM) sleep. However, this supposition has not been rigorously tested, and subjects older than infancy have not been studied. Given the differences in anatomy, physiology, and REM sleep distribution between infants and older children, and the reduced number of limb movements during REM sleep, we hypothesized that older subjects with CHS would have more severe hypoventilation during REM than NREM sleep. Nine subjects with CHS, aged (mean +/- SD) 13 +/- 7 yr, were studied. Spontaneous ventilation was evaluated by briefly disconnecting the ventilator under controlled circumstances. Arousal was common, occurring in 46% of REM vs. 38% of NREM trials [not significant (NS)]. Central apnea occurred during 31% of REM and 54% of NREM trials (NS). Although minute ventilation declined precipitously during both REM and NREM trials, hypoventilation was less severe during REM (drop in minute ventilation of 65 +/- 23%) than NREM (drop of 87 +/- 16%, P = 0.036). Despite large changes in gas exchange during trials, there was no significant change in heart rate during either REM or NREM sleep. We conclude that older patients with CHS frequently have arousal and central apnea, in addition to hypoventilation, when breathing spontaneously during sleep. The hypoventilation in CHS is more severe during NREM than REM sleep. We speculate that this may be due to increased excitatory inputs to the respiratory system during REM sleep.     

Matching in studies of classification accuracy: implications for analysis, efficiency, and assessment of incremental value

Summary.   In case–control studies evaluating the classification accuracy of a marker, controls are often matched to cases with respect to factors associated with the marker and disease status. In contrast with matching in epidemiologic etiology studies, matching in the classification setting has not been rigorously studied. In this article, we consider the implications of matching in terms of the choice of statistical analysis, efficiency, and assessment of the incremental value of the marker over the matching covariates. We find that adjustment for the matching covariates is essential, as unadjusted summaries of classification accuracy can be biased. In many settings, matching is the most efficient covariate-dependent sampling scheme, and we provide an expression for the optimal matching ratio. However, we also show that matching greatly complicates estimation of the incremental value of the marker. We recommend that matching be carefully considered in the context of these findings.     

Seed germination traits of Ailanthus altissima,Phytolacca americana and Robinia pseudoacacia in response to different thermal and light requirements

Invasion of alien plant species (IAS) represents a serious environmental problem, particularly in Europe, where it mainly pertains to urban areas. Seed germination traits contribute to clarification of invasion dynamics. The objective of this research was to analyze how different light conditions (i.e., 12-hr light/12-hr darkness and continuous darkness) and temperature regimes (i.e., 15/6°C, 20/10°C and 30/20°C) trigger seed germination of Ailanthus altissima (AA), Phytolacca americana (PA) and Robinia pseudoacacia (RP). The relationship between seed germination and seed morphometric traits was also analyzed. Our findings highlight that temperature rather than light was the main environmental factor affecting germination. RP germinated at all tested temperatures, whereas at 15/6°C seeds of AA and PA showed physiological dormancy. RP had a higher germination capacity at a lower temperature, unlike AA and PA, which performed better at the highest temperatures. Light had a minor role in seed germination of the three species. Light promoted germination only for seeds of PA, and final germination percentage was 1.5-fold higher in light than in continuous darkness. Seed morphometric traits (thickness [T], area [A] and volume [V]) had a significant role in explaining germination trait variations. The results highlight the importance of increasing our knowledge on seed germination requirements to predict future invasiveness trends. The increase in global temperature could further advantage AA and PA in terms of germinated seeds, as well as RP by enhancing the germination velocity, therefore compensating for a lower germination percentage of this species at the highest temperatures.     

Interplay between mobility,multi-seeding and lockdowns shapes COVID-19 local impact

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.     

Structure and modeling studies of the carboxy-terminus region of human tropoelastin.

Elastin macromolecular assembly is a highly complex mechanism involving many steps including coacervation, cross-linking, and probably other (not known) phenomena. In past studies, it has been proposed that the C-terminal part of tropoelastin is also involved in this process and may play a key role in tropoelastin interactions with other proteins of the final elastic fibres scaffold. Presented here are the results of the biophysical studies (biospectroscopy, bioinformatics) of the C-terminal domain of tropoelastin. We report the detailed structures adopted by the oxidized (native) and reduced forms of the free synthetic peptide with sequence encoded by exon 36 of human tropoelastin (GGACLGKACGRKRK) and propose a dynamical interpretation of which structures may be involved in interactions with other extra-cellular matrix proteins. We also suggest that these structures may be retrieved in other proteins sharing a consensus sequence; however no definitive conclusion can be drawn here on a possible structure-function relationship.     

A Parametric ROC Model‐Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies

Summary The predictiveness curve shows the population distribution of risk endowed by a marker or risk prediction model. It provides a means for assessing the model's capacity for stratifying the population according to risk. Methods for making inference about the predictiveness curve have been developed using cross‐sectional or cohort data. Here we consider inference based on case–control studies, which are far more common in practice. We investigate the relationship between the ROC curve and the predictiveness curve. Insights about their relationship provide alternative ROC interpretations for the predictiveness curve and for a previously proposed summary index of it. Next the relationship motivates ROC based methods for estimating the predictiveness curve. An important advantage of these methods over previously proposed methods is that they are rank invariant. In addition they provide a way of combining information across populations that have similar ROC curves but varying prevalence of the outcome. We apply the methods to prostate‐specific antigen (PSA), a marker for predicting risk of prostate cancer.     

Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve

Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.